\section{Literature Review}
\label{review}

How to select the feature is the key problem to the successful people detection\cite{Dollar:2012je}. Researchers usually utilize local features that are invariant to illumination and small deformations. Dalal's HOG \cite{Dalal:2006kz} is one of the most popular features used.  Zhu\cite{Zhu:2006vi} use integral histograms to speed up HOG. 
%Dollar\cite{Dollar:2010uv} present the integral Haar-like feature over multiple color channels. 

Instead of using only single feature like HOG, learning on bag-of-features shows better performance. Wang\cite{wang:2009} propose the HOG-LBP(Local Binary Pattern) feature that gain more improvements than HOG alone. 
%Geronimo\cite{Geronimo:2007} combine the Haar features into edge orientation histogram to train a Adaboost classifier. 
Geismann combine HOG with Haar feature in a cascade to detect pedestrians is benefit from the fast computation of Haar and accurate performance of HOG\cite{Geismann:2008}. 
Wu and Nevatia propose the Edgelet\cite{Wu:2008CVPR} and combine HOG and covariance features to detect human.  
%Tuzel\cite{Tuzel:2006} propose the method of using covariance matrix over various of features.
\cite{Wojek:2009eg} present a combination of Haar, Shapelet
%\cite{Sabzmeydani:2007} 
and HOG features, and show better detection than any other individual feature. Other researches has tried to describe the head-shoulder using omega-shape\cite{Li:2009cb} which inspire us to build the model on this basis.

Motion information have been proven an important factor for human visual understanding. It has used to remove false alarm, refine the result and thus increase the detecting speed. Xu\cite{Xu:2012} segment the image using motion cue, and refine the HOG detection result by motion distribution.  
%Senst\cite{Senst:2011tc} use Gaussian mixture motion model for the carrying object detection. Garcia-Martin\cite{GarciaMartin:2012gy}  utilize the visual ISM and motion MISM features to detect human in crowd. 
In \cite{Wang:2012ut}, motion information refine the result after the detection of shape figures and shadow.  
%When it comes to the modelling the human body, recent research work is mainly categorized into two classes: holistic and part-based. Holistic features are mostly introduced above. In part-based detection, \cite{Felzenszwalb:2010ve} is among the top detectors.Extension on HOG on deformable model makes them the state-of-art.
%In \cite{Wu:2008gf}, individual parts are detected and the Bayesian combination of parts construct the pedestrians. \cite{Lin:2010eu} build part-template tree and match the object hierarchically to detect human and their poses. 
%When occlusion becomes a serious problem, people turn to detect the head and shoulder instead of whole body\cite{Li:2009cb}.
%\cite{Zheng:wq,Aziz:2011ui,Li:2009cb}. 
%\cite{Wang:2011CE} apply the variabl-size HOG on mobile phone to capture heads.
%Zheng\cite{Zheng:wq} model the heads by omega-shape and detect the omega contour. 
%To reduce dimension of features, Zeng\cite{Zeng:2010PR} perform PCA to the HOG-LBP features on heads counting.
HOOF has been mostly used at action recognition\cite{Laptev:2008PR}.
Besides\cite{Dalal:2006kz}, which compute histogram of motion flow to detect humans on moving camera. HOOF has been applied mostly to action recognition area\cite{Wang:2011CE}
%,Wang:2012dj}. 
%Lee\cite{lee:2012} extract histogram from motion information to segment objects which is suitable for hardware design. 
\cite{Li:2011} design rule to absorb small blocks of HOOF to segment the flow of crowd.  
